Ethical AI is Very Complicated

There are signs of anxiety about artificial intelligence that are well grounded but also “Luddite.” AI concerns do include a legitimate focus on job markets, fairness, and safety. 

Job automation, economic inequality, bias, privacy, deepfakes, loss of human agency, concentration of power and longer-term potential risks are rational concerns.

On the other hand, there also is some admixture of resistance or skepticism about a new technology that might be shaped, but hardly seems possible to “stop.”

The signs are obvious:

• college students 

• younger workers

• community activists

• local residents

• political activists. 

What separates the "good" use of any technology and the "evil" use of that same technology? 

The simple answer is that most technology is morally inert. Human Intention is what separates the impact. 

But there is a sense in which “intention alone” is insufficient:

• Consequences matter independently of intent, which is why we have product liability laws

• At least some technologies are not entirely “neutral”

• landmines

• social media algorithms optimized for engagement

• Negligence (moral responsibility also extends to “what you should have foreseen”

• Externalities (climate, opioid addiction)

•  "Dual-use" (encryption; gain-of-function research) . 

So “intention is a “necessary but not sufficient” criteria for evaluating ethical implications. A fuller account could include:

• Design (What uses does the technology structurally enable or constrain?)

• Foreseeability (What harms were predictable?)

• Who benefits and who bears the risks?

• Systemic effects at scale.

So “intention” is the most important single factor in moral evaluation, but design, “affordances” (any property or feature of an object or environment that suggests and enables a specific action) and systemic effects generate moral responsibilities that exist independently of what anyone "meant."

Intent matters. But so do other consequences. The issue is how to create protections without weaponizing them (over-regulating; stifling; creating undue product liability laws).

AI Bottlenecks Might be Shifting

The artificial intelligence buildout has reached the networking layer. Cisco's latest quarterly financial report provides an example.  

For three years, the capital expenditure story has been about GPUs. NVIDIA's data center revenue dominated the narrative. Hyperscalers committed hundreds of billions to compute clusters. But a GPU cluster without networking infrastructure is a warehouse full of processors that cannot talk to each other. Training runs that span tens of thousands of GPUs require switching fabrics that move data between them at speeds measured in terabits per second. Every additional GPU added to a cluster multiplies the networking demand, because the communication overhead scales faster than the compute scales linearly.

The AI infrastructure value chain is filling in a predictable order. Compute came first: NVIDIA's data center revenue grew from $15 billion in fiscal 2023 to nearly $200 billion in fiscal 2026, a roughly thirteenfold increase in three years. Networking is arriving now: Cisco's AI infrastructure run rate just quadrupled year over year. Storage will follow.

The bottlenecks are  moving down the stack from processors to the physical infrastructure that connects them. The backplane is where the bottleneck lives.

CAPE is an Issue, But How Much?

Nobody can know for certain--beyond the fact that U.S. financial markets are in historically above-average valuation levels--what could happen next. 

Some rationally expect a reversion to mean, which will mean lower valuations.

Others just as rationally argue that above-average valuations can persist for some time, and that a correction is not in store. AI might be among the reasons, if it changes growth expectations.

source:  Ark Invest

Consider the Cyclically Adjusted Price-to-Earnings Ratio (CAPE), widely considered a valuable long-term valuation metric. The current CAPE is high, suggesting caution and a likely correction to lower levels.  

But many analysts believe that changes in accounting rules since the early 2000s make the standard version look artificially high relative to its historical average. Adjusted, it might still be high, but not at internet bubble levels. 

The CAPE is calculated as the current S&P 500 Price divided by the average of 10 years of inflation-adjusted earnings.

The issue is that the denominator uses reported GAAP earnings, and those earnings have become more conservative over time, leading to a boost in CAPE that make comparisons with past levels misleading, the argument goes. 

Key accounting changes include: 

• Goodwill impairment rules (FAS 142, adopted in 2001)

• Large acquisition write-downs now hit earnings immediately.

• Before 2001, many such costs were spread over decades.

• This depresses modern earnings compared with earlier periods.

• Mark-to-market accounting

•  lk;juring crises, companies must recognize large non-cash losses.

• These can sharply reduce earnings even if long-term economics are less affected.

• One-time charges

• Restructuring costs and impairments are recognized more aggressively.

The result is that the denominator in today’s CAPE is lower than it would have been under earlier accounting rules, making the ratio appear higher.

Economist Jeremy Siegel argues for using National Income and Product Accounts instead of GAAP earnings, to better normalize over time. 

The standard CAPE can overstate market valuation materially because recent earnings include unusually large accounting write-downs by roughly 10 percent to 25 percent.

Others argue for using operating earnings rather than reported earnings, which also can adjust earnings by 15 percent.

Estimated Distortion	Standard CAPE 38	Adjusted CAPE
10%	38.0	34.2
15%	38.0	32.3
20%	38.0	30.4
25%	38.0	28.5

Using such methods, the market still appears expensive, but less so than it might appear. 

Other issues:

• Lower interest rates over long periods

• Higher profit margins

• Global diversification of large U.S. firms

• Greater use of stock buybacks instead of dividends

• Stronger institutional ownership and retirement savings flows.

These factors may justify a structurally higher "normal" CAPE than the 19th- and 20th-century average.

So some will argue a practical adjustment for accounting changes is to reduce the published Shiller P/E by 10 percent to 25 percent.

This suggests the market may still be richly valued, but not as dramatically overvalued as the unadjusted Shiller P/E implies.

It is a useful gauge of long-term valuation, but it is not a short-term market timing tool, as history shows that markets can continue to rise for years, even when the CAPE ratio is well above its historical average.

Several forces can keep markets rising despite expensive valuations:

• Earnings continue to grow

• Corporate profits may rise fast enough to justify higher prices

• Investor optimism and momentum

• Strong sentiment can sustain elevated valuations for extended periods

• Low interest rates

• When bond yields are low, investors are willing to pay more for equities

• New technologies can create expectations of stronger future growth

• Retirement contributions, buybacks, and institutional inflows can support prices.

Period	Approximate CAPE at Start	Years Until Major Peak	Additional Market Gain After CAPE Became Elevated	What Happened
1925–1929	25–32	4 years	+150% to +200%	Roaring Twenties speculation pushed valuations higher before the 1929 crash
1995–2000	25–44	5 years	+200% to +250%	Dot-com bubble drove extraordinary gains
2017–2021	30–38	4 years	+80% to +120%	Continued growth in Apple Inc., Microsoft Corporation, NVIDIA Corporation and other large-cap firms
2023–2026	Mid-30s (approx.)	Ongoing	Still developing	Strong enthusiasm around artificial intelligence and large technology firms

The point is that valuation is a poor short-term timing tool:

• A CAPE above average tells you expected long-term returns may be lower, but it does not predict when prices will stop rising

• Markets can stay expensive for years

• If profits rise rapidly, high valuations can become more sustainable

• Structural changes matter (lower inflation, global market reach, and dominant technology companies may justify higher valuation ranges than in earlier eras).

We still have to make our own choices about timing, though!

Cutting Opex to Support AI Capex Probably Sets Stage for More of the Same

In a brutal way, the notion that the business value of artificial intelligence hinges in large part on displacing human labor has already begun to play out in early substitution of capital expense for operating expense.

For the top five U.S. hyperscalers (Alphabet, Amazon, Meta, Microsoft, and Oracle), combined capex is projected to approach $725  billion in 2026, a more than 60 percent increase over 2025 levels.

To fund this without destroying their balance sheets, companies are pulling two primary levers:

1. Direct Labor Substitution: Reducing headcount in "legacy" or non-core divisions to reallocate those billions into AI infrastructure.

2. Productivity Gains: Using AI internally ("vibe coding" or AI-assisted programming) to handle the same workload with fewer new hires.

Company	Estimated Jobs Cut	Timeframe
Oracle	~30,000	Q1 2026
Amazon (AWS/Corp)	~16,000–30,000	Q1-Q2 2026
Meta	~16,000	Q2 2026
Dell	~11,000	Fiscal 2026
Cisco	~6,000	Q2 2026
Microsoft/LinkedIn	~8,750 (Buyouts)	Q2 2026
Google Cloud	~1,500	Ongoing 2026

Analysts at firms like Goldman Sachs and J.P. Morgan argue that while the headcount reduction is painful, the "cost of doing nothing" is higher. 

And while overspending represents a danger, firms are cutting headcount to pay for the capital investments. 

The logic is indeed "brutal" because it substitutes capex for human employment (operating expense). 

On the other hand, that is the simple logic of most computing technology deployments.

UGC Created Social Media; Might AI Create "Social Software?"

Are investors and financial markets consistently rational or likely to be correct where it comes to the valuation of high-performance computing infrastructure investments and the likely impact on enterprise software as a service?

Investors seem to be simultaneously betting that AI capex is "too high" and won't produce big benefits, while simultaneously betting that software as a service will be disrupted successfully by AI. 

Can both outcomes occur at once? 

• If AI is ineffective, then the high capex won’t affect SaaS

• If SaaS can be disrupted, then the AI is effective. 

So markets are betting that an “ineffective means” (high AI infrastructure capex) produces an effective outcome (SaaS is disrupted). 

In that scenario, high capex “fails” but then SaaS disruption “succeeds.”

The scenario the market appears not to believe is that high AI capex will prove effective, even in the short term, but that SaaS providers are able to avoid disruption because “write your own app” is not a long-term source of value. 

The business moats for enterprise SaaS lie elsewhere. 

Under what conditions might both be right; both be wrong?

Some might argue the answer is that, “yes, both arguments can be correct, at the same time.”

In this scenario, the "capex is too high" argument could be right if hyperscalers and suppliers of neocloud services are overspending on hardware that they cannot monetize profitably at current margins.

Simultaneously, the "SaaS is disrupted" argument might also be right because AI replaces relatively more expensive software as a service with "service-as-software" in the form of a cheap, autonomous agent.

But it is possible both theses are wrong. 

Perhaps AI increases the value of SaaS. Perhaps:

• The demand for AI turns out to be even larger than anticipated (every dollar of GPU spend generates five dollars of high-margin revenue)

• Incumbent SaaS companies (Salesforce, ServiceNow, Adobe) successfully "wrap" AI into their existing workflows. 

• Users prove they do not want to use 10,000 separate AI agents

• The SaaS "moat" proves to be the proprietary data and the user workflow

• Hyperscalers cut headcount to fund AI capex, with relatively-neutral cash flow implications medium term. 

Outcome	Condition for Capex Validity	Condition for SaaS Survival
Optimism is Rational	AI leads to "Artificial General Intelligence" (AGI) levels of productivity.	SaaS companies fail to innovate, and "Agentic" workflows replace seat-based licenses.
Optimism is Irrational	We are in an "AI infrastructure bubble” and financial returns will not emerge.	Investors overestimate AI's ability to handle complex, m.essy, human-centered business logic.

The market might be irrational, in other words, creating a valuation opportunity:

• Yes, capex is high, but warranted: “market leaders” are being born

• Other means will be found to reduce pressure on free cash flow

• SaaS valuations already have reset, and suppliers will successfully respond

• SaaS value is not based on code, but other variables. 

The analogy might be user-generated content. It might once have been feared it would be a substitute for “professional” content. But that did not happen. Social media emerged as a distinct experience. 

In the mid-2000s, the debate was: Will blogs replace newspapers? or Will YouTube replace TV?

When the cost of "publishing" went to zero, we didn't just get the same news in a different format; we got a massive explosion of new content types (influencers, viral memes, and real-time citizen journalism) that professional studios could never have produced.

Feature	The Social Media Shift (Web 2.0)	The AI Shift (Generative Era)
The "Threat"	UGC replacing Professional Media.	AI replacing SaaS/Labor.
The Reality	Expanded the total volume of content by orders of magnitude.	Expands the total volume of "work/code" by orders of magnitude.
The New Form	The Feed (Algorithmically curated attention).	The Agent (Autonomous execution of intent).

If SaaS is about providing a tool for a human to do work (Salesforce is a tool for a salesperson), the new form of AI is about providing the outcome itself.

Instead of primarily displacing SaaS, AI is enabling a "service-as-software" or maybe “social software” model:

• Hyper-personalized software: AI might "spin up" a bespoke, ephemeral interface tailored to a specific project’s needs, then dissolve it when the task is done

• Shadow workers: In social media, everyone became a creator. In the AI era, everyone becomes a "manager" or "orchestrator." The "something else" is a world where a single person can run a complex operation that previously required a department of 50

• Micro-services: SaaS was always limited by "Total Addressable Market" (TAM). If a problem was too small, no one built software for it. AI can make it profitable to solve problems that were previously too "minor" to automate.

So we might be looking for signs that “social media” is developing. Perhaps it is not so much that SaaS is disrupted as it is that “social software” is developing.